518 research outputs found
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for different classes. Meanwhile, we introduce group
regularization to enforce representations to preserve label properties of
original samples, meaning the representations for the same class are encouraged
to be similar. Upon the cross-label suppression, we don't resort to
frequently-used -norm or -norm for coding, and obtain
computational efficiency without losing the discriminative power for
categorization. Moreover, two simple classification schemes are also developed
to take full advantage of the learnt dictionary. Extensive experiments on six
data sets including face recognition, object categorization, scene
classification, texture recognition and sport action categorization are
conducted, and the results show that the proposed approach can outperform lots
of recently presented dictionary algorithms on both recognition accuracy and
computational efficiency.Comment: 36 pages, 12 figures, 11 table
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Sparse representations using overcomplete dictionaries have proved to be a
powerful tool in many signal processing applications such as denoising,
super-resolution, inpainting, compression or classification. The sparsity of
the representation very much depends on how well the dictionary is adapted to
the data at hand. In this paper, we propose a method for learning structured
multilevel dictionaries with discriminative constraints to make them well
suited for the supervised pixelwise classification of images. A multilevel
tree-structured discriminative dictionary is learnt for each class, with a
learning objective concerning the reconstruction errors of the image patches
around the pixels over each class-representative dictionary. After the initial
assignment of the class labels to image pixels based on their sparse
representations over the learnt dictionaries, the final classification is
achieved by smoothing the label image with a graph cut method and an erosion
method. Applied to a common set of texture images, our supervised
classification method shows competitive results with the state of the art
Robust Sparse Coding via Self-Paced Learning
Sparse coding (SC) is attracting more and more attention due to its
comprehensive theoretical studies and its excellent performance in many signal
processing applications. However, most existing sparse coding algorithms are
nonconvex and are thus prone to becoming stuck into bad local minima,
especially when there are outliers and noisy data. To enhance the learning
robustness, in this paper, we propose a unified framework named Self-Paced
Sparse Coding (SPSC), which gradually include matrix elements into SC learning
from easy to complex. We also generalize the self-paced learning schema into
different levels of dynamic selection on samples, features and elements
respectively. Experimental results on real-world data demonstrate the efficacy
of the proposed algorithms.Comment: submitted to AAAI201
Semi-supervised dual graph regularized dictionary learning
In this paper, we propose a semi-supervised dictionary learning method that
uses both the information in labelled and unlabelled data and jointly trains a
linear classifier embedded on the sparse codes. The manifold structure of the
data in the sparse code space is preserved using the same approach as the
Locally Linear Embedding method (LLE). This enables one to enforce the
predictive power of the unlabelled data sparse codes. We show that our approach
provides significant improvements over other methods. The results can be
further improved by training a simple nonlinear classifier as SVM on the sparse
codes.Comment: in Proceedings of iTWIST'18, Paper-ID: 33, Marseille, France,
November, 21-23, 201
Multi-View Task-Driven Recognition in Visual Sensor Networks
Nowadays, distributed smart cameras are deployed for a wide set of tasks in
several application scenarios, ranging from object recognition, image
retrieval, and forensic applications. Due to limited bandwidth in distributed
systems, efficient coding of local visual features has in fact been an active
topic of research. In this paper, we propose a novel approach to obtain a
compact representation of high-dimensional visual data using sensor fusion
techniques. We convert the problem of visual analysis in resource-limited
scenarios to a multi-view representation learning, and we show that the key to
finding properly compressed representation is to exploit the position of
cameras with respect to each other as a norm-based regularization in the
particular signal representation of sparse coding. Learning the representation
of each camera is viewed as an individual task and a multi-task learning with
joint sparsity for all nodes is employed. The proposed representation learning
scheme is referred to as the multi-view task-driven learning for visual sensor
network (MT-VSN). We demonstrate that MT-VSN outperforms state-of-the-art in
various surveillance recognition tasks.Comment: 5 pages, Accepted in International Conference of Image Processing,
201
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
We propose a novel structured discriminative block-diagonal dictionary
learning method, referred to as scalable Locality-Constrained Projective
Dictionary Learning (LC-PDL), for efficient representation and classification.
To improve the scalability by saving both training and testing time, our LC-PDL
aims at learning a structured discriminative dictionary and a block-diagonal
representation without using costly l0/l1-norm. Besides, it avoids extra
time-consuming sparse reconstruction process with the well-trained dictionary
for new sample as many existing models. More importantly, LC-PDL avoids using
the complementary data matrix to learn the sub-dictionary over each class. To
enhance the performance, we incorporate a locality constraint of atoms into the
DL procedures to keep local information and obtain the codes of samples over
each class separately. A block-diagonal discriminative approximation term is
also derived to learn a discriminative projection to bridge data with their
codes by extracting the special block-diagonal features from data, which can
ensure the approximate coefficients to associate with its label information
clearly. Then, a robust multiclass classifier is trained over extracted
block-diagonal codes for accurate label predictions. Experimental results
verify the effectiveness of our algorithm.Comment: Accepted at the 28th International Joint Conference on Artificial
Intelligence(IJCAI 2019
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
In this paper, we propose an analysis mechanism based structured Analysis
Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates
the analysis discriminative dictionary learning, analysis representation and
analysis classifier training into a unified model. The applied analysis
mechanism can make sure that the learnt dictionaries, representations and
linear classifiers over different classes are independent and discriminating as
much as possible. The dictionary is obtained by minimizing a reconstruction
error and an analytical incoherence promoting term that encourages the
sub-dictionaries associated with different classes to be independent. To obtain
the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on
the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm
constraint applied in most existing DL criteria makes the training phase time
consuming. The codes-extraction projection that bridges data with the sparse
codes by extracting special features from the given samples is calculated via
minimizing a sparse codes approximation term. Then we compute a linear
classifier based on the approximated sparse codes by an analysis mechanism to
simultaneously consider the classification and representation powers. Thus, the
classification approach of our model is very efficient, because it can avoid
the extra time-consuming sparse reconstruction process with trained dictionary
for each new test data as most existing DL algorithms. Simulations on real
image databases demonstrate that our ADDL model can obtain superior performance
over other state-of-the-arts.Comment: Accepted by IEEE TNNL
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
This paper presents a structured dictionary-based model for hyperspectral
data that incorporates both spectral and contextual characteristics of a
spectral sample, with the goal of hyperspectral image classification. The idea
is to partition the pixels of a hyperspectral image into a number of spatial
neighborhoods called contextual groups and to model each pixel with a linear
combination of a few dictionary elements learned from the data. Since pixels
inside a contextual group are often made up of the same materials, their linear
combinations are constrained to use common elements from the dictionary. To
this end, dictionary learning is carried out with a joint sparse regularizer to
induce a common sparsity pattern in the sparse coefficients of each contextual
group. The sparse coefficients are then used for classification using a linear
SVM. Experimental results on a number of real hyperspectral images confirm the
effectiveness of the proposed representation for hyperspectral image
classification. Moreover, experiments with simulated multispectral data show
that the proposed model is capable of finding representations that may
effectively be used for classification of multispectral-resolution samples.Comment: 16 pages, 9 figure
Contextual Explanation Networks
Modern learning algorithms excel at producing accurate but complex models of
the data. However, deploying such models in the real-world requires extra care:
we must ensure their reliability, robustness, and absence of undesired biases.
This motivates the development of models that are equally accurate but can be
also easily inspected and assessed beyond their predictive performance. To this
end, we introduce contextual explanation networks (CEN)---a class of
architectures that learn to predict by generating and utilizing intermediate,
simplified probabilistic models. Specifically, CENs generate parameters for
intermediate graphical models which are further used for prediction and play
the role of explanations. Contrary to the existing post-hoc model-explanation
tools, CENs learn to predict and to explain simultaneously. Our approach offers
two major advantages: (i) for each prediction valid, instance-specific
explanation is generated with no computational overhead and (ii) prediction via
explanation acts as a regularizer and boosts performance in data-scarce
settings. We analyze the proposed framework theoretically and experimentally.
Our results on image and text classification and survival analysis tasks
demonstrate that CENs are not only competitive with the state-of-the-art
methods but also offer additional insights behind each prediction, that can be
valuable for decision support. We also show that while post-hoc methods may
produce misleading explanations in certain cases, CENs are consistent and allow
to detect such cases systematically.Comment: 48 pages, 18 figures, to appear in JML
Semi-supervised Sparse Representation with Graph Regularization for Image Classification
Image classification is a challenging problem for computer in reality. Large
numbers of methods can achieve satisfying performances with sufficient labeled
images. However, labeled images are still highly limited for certain image
classification tasks. Instead, lots of unlabeled images are available and easy
to be obtained. Therefore, making full use of the available unlabeled data can
be a potential way to further improve the performance of current image
classification methods. In this paper, we propose a discriminative
semi-supervised sparse representation algorithm for image classification. In
the algorithm, the classification process is combined with the sparse coding to
learn a data-driven linear classifier. To obtain discriminative predictions,
the predicted labels are regularized with three graphs, i.e., the global
manifold structure graph, the within-class graph and the between-classes graph.
The constructed graphs are able to extract structure information included in
both the labeled and unlabeled data. Moreover, the proposed method is extended
to a kernel version for dealing with data that cannot be linearly classified.
Accordingly, efficient algorithms are developed to solve the corresponding
optimization problems. Experimental results on several challenging databases
demonstrate that the proposed algorithm achieves excellent performances
compared with related popular methods
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